Generally, an optimal receiver for a Multiple-Input Multiple-Output (MIMO) system is a joint maximum likelihood (JML) receiver of a channel and a MIMO detector. However, the optimal receiver may not be embodied in an aspect that the complexity of the optimal receiver exponentially increases with respect to a number of transmit antennas, a modulation/demodulation order, and a packet length.
A turbo receiver has been proposed to reduce the complexity of the optimal receiver. The turbo receiver consists of a maximum a posteriori (MAP) MIMO detector and a MAP channel decoder. The turbo receiver obtains performance similar to the optimal receiver by repeatedly exchanging extrinsic information between the MAP MIMO detector and the MAP channel decoder.
However, although the complexity of the conventional turbo receiver has been reduced in comparison to the optimal receiver, the complexity of the conventional turbo receiver still remains high. Therefore, various ways for reducing the complexity of the turbo receiver are being studied. In particular, researches regarding reducing the complexity of the MAP detector are being conducted.
A minimum mean squared error with soft cancellation (MMSE-SC) detector is known as the simplest way for reducing the complexity of the MAP detector. However, when the MMSE-SC detector is used, the complexity can be reduced whereas the performance can be deteriorated in comparison to the original MAP detector.
In the case of using the MAP detector, extrinsic information needs to be input. If a posteriori information with a larger amount of information than the extrinsic information is used, the turbo receiver may be diverged. Based on the above grounds, when the MMSE-SC detector is used, the extrinsic information is input as an input signal.
Also, there is another great disadvantage in that the power consumption is relatively great since the turbo receiver requires iterative operations.
Accordingly, there is a need for a method that can improve the reception performance and also can reduce the complexity and the power consumption.